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On the Impact of Coverage Range on AIS Message
Reception at Flying Platforms
Federico Clazzer‡ , Andrea Munari‡ , Simon Plass‡ and Birgit Suhr∗
‡
DLR - German Aerospace Center, Institute of Communications and Navigation - Wessling, Germany
∗
DLR - German Aerospace Center, Institute of Space Systems - Bremen, Germany
{federico.clazzer, andrea.munari, simon.plass, birgit.suhr}@dlr.de
Abstract—In the recent past, an increasing interest has been
devoted to the possibility of receiving Automatic Identification
System (AIS) messages via Low Earth Orbit (LEO) satellites.
While the principle has been demonstrated to be a viable option
for monitoring vessel traffic over oceans and vaste land areas,
the achievable performance from a communications viewpoint is
far from optimal. Recently, it was shown how AIS traffic seen
at a satellite can be very accurately modeled resorting to simple
random access schemes. Leveraging this result, in this work we
propose a simple yet flexible analytical framework capable of
predicting channel load and overall reception performance taking
into account the spatial distribution of vessels as well as their
traffic generation pattern. Feeding the model with ship speed
and location data derived from experimental settings, we discuss
the achievable efficiency for a typical LEO-satellite detecting AIS
packets. Moreover, the impact of the receiver footprint on ground
on the overall decoding performance is investigated, deriving
some interesting insights on the benefits that could stem resorting
to narrower-beam systems. In this direction, we discuss two cases:
the usage of a LEO satellite with a directional antenna soon to be
launched for AIS monitoring, and the possibility of using airliner
for receiving vessel-generated traffic.
I.
I NTRODUCTION
In the last years, satellite-aided Automatic Identification
System (AIS) has become one of the hot topics in the maritime safety and security field, drawing the attention of both
researchers and standardization bodies [1]. The AIS standard
[2], developed in the 1990’s and now a mandatory feature
for commercial vessels in most countries, dictates boats to
periodically broadcast in the VHF band location and speed
information, which can be used by surrounding vessels and by
operators in the mainland to prevent collisions and make local
traffic decisions.
A major breakthrough in the field was achieved in the mid
2000’s, when several studies proved the viability of AIS message reception at Low Earth Orbit (LEO) satellites [3], [4], [5],
[6], [7]. As a matter of fact, the possibility to collect navigation
information at a flying platform complements the original goal
of AIS, providing an overall picture of vessels distributed over
large areas and enabling worldwide routes monitoring. This, in
turn paved the way to an unexplored plurality of commercial
applications which are today an expanding reality, ranging
from goods and ship tracking to oceans monitoring for both
environmental and safety purposes.
Despite the increasing attention devoted to research and
development in the field, reception of AIS messages at a
satellite is in general not yet efficient from a communications
perspective. The standard, in fact, was originally devised
to enable reliable data exchanges among a relatively small
population of vessels, and resorts to a medium access scheme
which distributedly forms clusters where messages are sent
in a coordinated fashion. On the other hand, when a satellite
footprint covering areas of several thousands of square kilometers is considered, the contribution of many clusters not
in visibility of each other - and thus not coordinated - will
result in packet collisions at the receiver, with detrimental
effects on decoding performance. Especially in densely ship
populated regions this issue affects dramatically the tracking
performance of a satellite-aided AIS system, all the more so
considering the steadily increasing traffic generated by other
maritime communication services being allocated to VHF band
[8].
An interesting characterization of the problem was given in
[9], where the authors showed by means of a simple analytical
framework how incoming AIS traffic at a Low Earth Orbit
(LEO) satellite can be very accurately modeled considering
a slotted Aloha access scheme that disregards any form of
coordination among vessels. Taking the lead from this, and
relying on experimental data for ships distribution, it was
possible to prove how the average load to be expected in
regions of interest such as the Mediterranean see or the western
coasts of Europe can easily be very high (e.g, larger than 5
pk/slot). Therefore, although some recent works have tried
to improve decoding capabilities at the satellite resorting to
advanced signal processing techniques [10], it is clear that
a limiting factor to the overall achievable performance is
given by the footprint used to collect AIS messages. In this
perspective, an interesting and key tradeoff arises: on the one
hand a larger coverage radius is desirable, so to get a snapshot
on a wider area; on the other hand, the broader the footprint,
the higher the traffic and the lower the decoding probability.
Within this paper, we study this tradeoff in greater detail,
deriving some insights of interest both from a research and a
practical viewpoint. Extending the work in [9], we develop
an analytical model to characterize relevant metrics such
as throughput and detection probability. To achieve this we
work at packet level, considering collisions among messages
as destructive and abstracting physical layer details. While
such an approach is particularly useful to derive closedform expressions that capture the key parameters coming into
play, it also provides a reasonable first approximation of the
performance proper of the rather simple physical layer of AIS.
The framework we introduce is rather flexible, and can take
as input any geographical density for vessels as well as any
velocity profile of interest. Within this work, in particular,
we present results obtained considering experimental data for
both aspects, and we elaborate on the decoding behavior at a
flying platform referring to three cases of practical relevance,
encompassing a typical LEO satellite with coverage radius of
approximately 2500 km; a satellite developed by the German
Aerospace Center (DLR) for launch in 2014 with a reduced
footprint; and a scheduled commercial aircraft, i.e., airliner,
flying over an area of interest.
We start our discussion in Section II by providing a short
description of some relevant aspects of the AIS communication
protocols. Then, Section III introduces an analytical framework
to evaluate under certain assumptions the load and throughput
achievable when decoding AIS packets at a flying object
characterized by a specific coverage range. The model is
subsequently used in Section IV to evaluate worldwide the
performance tradeoff induced by the reception radius under
realistic ship distribution and to identify possible optimization
schemes. The conclusions of our work are finally drawn in
Section V.
II.
AUTOMATIC I DENTIFICATION S YSTEM (AIS)
AIS was designed in the 90’s as a system for automatic vessel identification in order to improve safety of navigation and
increase the level of sea surveillance. While a comprehensive
description of the standard goes beyond the scope of this work,
we briefly report in this section an overview of some relevant
aspects of the communication protocol which are relevant for
the following discussion.
At the physical layer, two VHF channels around 160 MHz
(161.975 MHz and 162.025 MHz) are available, with 9.6 kbit/s
bit rate and Gaussian minimum-shift keying (GMSK) modulation. No Forward Error Correction (FEC) nor interleaving
is used [2]. Frames of one-minute duration and composed of
2250 slots repeat over time, and vessels are assumed to be
slot-synchronized, e.g., by means of a GPS signal. Within each
frame a ship can signal to its neighbors or to the costal authorities relevant information on its status, including position,
speed and direction updates by transmitting AIS messages of
the duration of one slot. In order to ensure a certain level of
reliability for packet delivery, the standard defines at the MAC
layer 4 different access schemes depending on the mode of
operation. The most used, and taken as reference throughout
this paper, is Self-Organized Time Division Multiple Access
(SOTDMA), foreseen for vessels traveling along a specific
route called continuous operation. SOTDMA is based on a
distributed clustering concept in which vessels within each
other’s coverage range distributively coordinate so to avoid
collisions among packets. In particular, each vessel’s AIS
receiver keeps track of the slot occupation with the help
of a dynamic directory of received neighbors, and sends its
messages only over slots that are perceived as free. Once a slot
is occupied by a ship, it may be booked for a certain number
of frames indicated in a time out field of the AIS message, so
to ease negotiation procedures.
The frequency with which ships send updates depends on
the speed, so to allow proper tracking and collision avoidance
capabilities. In particular, AIS defines 4 groups of frequencies
that shall be used by vessels. Ships travelling with a speed up
to 3 Knot (kn) send AIS packets every 180 s, ships travelling
with a speed between 3 kn and 14 kn send AIS packets every
10 s, ships travelling with a speed between 14 kn and 23 kn
send AIS packets every 6 s, while ships travelling with a speed
above 23 kn send AIS packets every 2 s.
III.
A C HARACTERIZATION OF AIS T RAFFIC
L OAD AT THE S ATELLITE
Throughout our investigation we focus on a flying platform, e.g., a satellite or an airliner, collecting AIS messages
transmitted by ships within its coverage range. As discussed
in Section II, time is divided in slots, each of them of the
duration of one data unit. Reception at the platform is modeled
at a packet level, abstracting the underlying physical layer,
and collisions are regarded as destructive. According to this
assumption, an AIS message sent by a vessel is successfully
retrieved only if no other peer accessed the channel over
the same slot. An exact characterization of the traffic pattern
generated as per SOTDMA by ship clusters falling within the
footprint of the receiver and not coordinating among each other
is in general not trivial. On the other hand, [9] showed that a
very good approximation can be obtained assuming all vessels
to simply generate messages according to a Poisson process
of aggregate intensity G and accessing the medium as soon as
data units are available for transmission. Leveraging this result,
we model the flying platform as the receiver in a well-known
Slotted ALOHA (SA) protocol.
This working hypothesis is particularly useful, since the
performance of the system can be extensively characterized
as soon as the channel load G, expressed in packets per
slot, is available. This parameter, in turn, depends on the
number of transmitters and on the message generation rate
associated to each of them, and brings the AIS traffic and
topology properties into the model. In the case of interest, in
fact, the transmitters population coincides with the number of
vessels n that fall within the footprint of the receiver, while
the transmission frequency ω (measured in packets per slot)
depends on the vessel speed, as defined in the AIS standard
[2]. The former parameter can be computed for any vessel
spatial distribution fS (ϕ, λ) over the Earth surface as:
n=
fS (ϕ, λ), dϕ dλ.
(1)
(ϕ,λ)∈A
where (ϕ, λ) represent the coordinates of a point in latitude
and longitude, and A is the region on the terrestrial sphere
described by the reception pattern of the flying platform.
Recalling that the standard foresees four different transmission
frequencies ωi for ships, the overall expected channel load can
be written as
G=E
ni ωi =
i
E[ni ]ωi ,
(2)
i
where ni is the number of ships that generate messages with
frequency ωi . In turn, ni can readily be expressed as soon as
the probability density function (PDF) fv of vessels speed is
available:
vi
fv (v)dv,
ni = n
vi−1
(3)
where [vi−1 ; vi ] is the velocity range in which AIS messages
are sent with frequency ωi .
Combining (1) and (3), we finally obtain:
vi
fS (ϕ, λ)dϕ dλ
G=
ωi
i
(ϕ,λ)∈A
fv (v)dv ,
(4)
vi−1
which allows to compute the channel load for any geographic
distribution and mobility model of interest. On the other hand,
(4) also highlights the dependency of G on the footprint,
which, as discussed in Section I, triggers a key tradeoff for
the overall system performance. A, in fact, corresponds to the
portion of the Earth surface that is covered by the receiver
when placed in a specific location, which depends in general
on the antenna coverage and on the height of the platform.
In order to further elaborate a general framework, let us
assume a circular receiver’s coverage area of radius r. Under
this hypothesis, the latitudes spanned by A can be easily
evaluated, as they only depend on r. On the contrary, the
lower and higher longitude integration endpoints in (4) (λmin
and λmax , respectively) are a function of both latitude and
radius r and can be evaluated with the help of the haversine
formula. In fact, if the receiver is located at (ϕc , λc ), any
point of coordinates (ϕ, λ) on the Earth surface lying on the
circumference of the footprint satisfies:
r = 2RE arcsin(a),
(5)
IV.
where RE is the Earth radius, and a is given by
a=
sin2
ϕ − ϕc
2
+ cos(ϕc ) cos(ϕ) sin2
λ − λc
,
2
With few mathematical manipulations, we can find the expression of the sought integration endpoints as the solutions λmin
and λmax of (5) as
λmin = min {λc + arccos(b), λc − arccos(b)}
λmax = max {λc + arccos(b), λc − arccos(b)} ,
where b is expanded as
sin2
b=1−2
r
2RE
− sin2
Fig. 1. Ship density from the database generated during the PASTA-MARE
project. The ship density is defined as the average number of vessel within a
grid cell, based on 10 global satellite AIS scenes. Each global satellite AIS
scene retains one position report per vessel within a time frame of 8 days
[11]. All grid points where 20 or more ships are present are reported as the
maximum scale colour in the figure. There are some heavily ship populated
areas, like the Baltic Sea, where the satellite data are not precise due to the
very high number of packet collisions experienced by the satellites collecting
AIS packets.
ϕ−ϕc
2
cos(ϕc ) cos(ϕ)
.
Plugging these values into (4), G can be finally computed for
any circular footprint of interest.
The simple analytical framework presented is then particularly handy, since it allows to compute the channel load
induced by AIS traffic for any given position of the receiver,
e.g., for a moving LEO satellite, as well as for different
coverage ranges and vessel movement profiles. Within the
next section, we will leverage this flexibility to evaluate the
achievable performance for different system configurations of
interest, with particular attention on the impact of the footprint
generated by different flying platforms.
C OVERAGE R ANGE AND AIS R ECEPTION
P ERFORMANCE
Although satellite detection of AIS messages is very
promising for future global vessel and goods tracking, a major
and intrinsic impairment to its efficiency is given by the
very extended covered footprint, which causes signals from
ships very far from each other to possibly overlap at the
receiver. In view of the lack of any form of channel coding,
packet collisions can in many cases be destructive, inducing
poor AIS decoding performance especially in heavily vessel
populated regions. Starting from these remarks, in recent years
an increasing interest has been drawn by the use of other
flying objects for receiving ship-generated information as a
complement for satellite-based AIS systems. Relevant results
in this direction were presented in [12], where reception at
aircraft of AIS messages was proven during flight trials at
altitudes of up to 10 km. The concept feasibility was further
stressed in [13] with an investigation on the coverage of
watered-sea areas by airliners showing how the majority of
vessel routes are also covered by airliner. On the other hand, an
interesting and complementary approach to reduce the satellite
footprint can be represented by advanced antenna design to
narrow the generated beam.
While relevant effort is being devoted to the development
of such solutions, a clear comparison among them in terms of
the critical tradeoff between the larger number of detectable
vessels and the lower decoding probability induced by a larger
footprint is still missing. From this viewpoint, the framework
developed in Section III is particularly useful, as it enables an
analytical evaluation of the achievable performance. To this
Fig. 2. Vessel speed PMF for ships close to coast. The PMF is derived from
the decoded AIS packets received from an aircraft flying at circa 10 km [12].
Fig. 3. Vessel CDF of the speed for both ships close to coast and ships
in open sea navigation. The coast vessel speed distribution is derived from
the AIS packets received during flight trials and elaborated in [12], while
the open sea speed distribution is a model derived from 100 ships traveling
towards open sea.
aim, three elements need to be fed to the model: a spatial
distribution of vessels within the area of interest, a speed
profile followed by ships, and a coverage radius offered by
the receiver.
As to the first aspect, we resort to the results of the
PASTA-MARE project [11], which offers an estimate of the
ship position density derived from experimental reception of
AIS data at a LEO satellite, as reported in Fig. 1. Real-world
data are also employed to describe the speed distribution of
vessels. In particular, throughout our analysis we characterize
differently the behavior of ships traveling offshore and close
to the coast. The former follow a speed profile derived from
data gathered from online databases for vessels in open seas or
oceans, while the speed of the latter is derived from detected
AIS data during the flight trials presented in [12]. In Fig.
2 the probability mass function of ship velocity for vessels
close to the coast is presented. The vast majority of them
have speeds up to 10 kn, as expected due to the proximity
to harbours, straits or other constrained areas. The open sea
and oceanic ships speed distribution is instead modeled as a
Gaussian random variable with mean 12.8 kn and σ = 5 kn.
In Fig. 3 the comparison of the coast vessel speed CDF and
the open sea vessel speed CDF is depicted. We can observe
that while for the coast vessel speed distribution the large
majority of vessel speeds are up to 10 kn, for the open sea
speed CDF the majority of vessel speeds are up to 15 kn and
that below 5 kn very few vessel can be found. Finally, for the
characterization of the receiver coverage range, we focus on
three cases of interest:
•
a common LEO satellite at the altitude of 524 km,
which offers a footprint of 2500 km (1349 Nautical
Miles (NM)) in radius;
•
a LEO satellite with an enhanced antenna design providing a narrower beam to reduce the incoming AIS
load. To this aim, we consider as reference the DLR
AISat-1 nanosatellite. The satellite, of approximately
13 kg in weight is equipped with a 4.2 m long HighGain helix antenna (see also Fig. 4) and four AIS
Fig. 4.
DLR’s AISat-1 with helix antenna.
receivers, developed by the Institute of Space Systems
of the German Aerospace Center (DLR) in Bremen.
The satellite has an optimized RF-Front End with an
HQ filter and 2 VHF dipole antennas. Additional components are a UHF beacon, 1 UHF antenna (437MHz)
and 2 UHF dipole antennas. The AISat-1 satellite will
be launched in 2014 into a sun-synchronized LEO
orbit at an altitude of 650 km on an Indian PSLV from
Sriharikota, India, and the resulting offered coverage
range is of approximately 350 km.
•
an aircraft flying at 10 km. Assuming that all ships
with elevation angle ≥ 0◦ are received from the
aircraft, which has to be shown realistic in [12], the
reception radius [13] is in fact r = arccos(RE /(RE +
h)) · RE ∼
= 356 km. By virtue of the very similar
footprint with respect to the AISat-1, the two cases
will not be distinguished in the following discussion.1
Let us then start our discussion focusing on the first
1 Even if offering the same coverage area, the AISat-1 and the airliner-based
solutions differ in two main relevant aspects: the altitude (much lower for the
airliner, and altering the power distribution profile of incoming AIS messages);
and the speed (much higher for the satellite, which covers a certain area for a
shorter time). The packet-level framework considered in this paper abstracts
these aspects, an investigation of which we leave as part of our future work.
Fig. 5. Channel load G in packets per slot seen at a LEO satellite. Each
point in the map represents the channel load seen at the LEO satellite located
in that specific longitude and latitude with a reception radius of 2500 km.
scenario. Fig. 5 reports the channel load for a LEO satellite
over a wide Earth surface area covering the range 70◦ S to 70◦ N
in latitude and 150◦ W to 70◦ E in longitude. Each point on the
map represent the channel load for a receiver located towards
that latitude and longitude which has a reception radius of
2500 km. We can observe that the maximum channel load at
the satellite can exceed 12 packets per slot in the Caribbean
area close to Florida and Cuba and can be very high, 8 or
more packets per slot, also in other regions like Portuguese and
Spanish coasts as well as Mediterranean Sea, Gulf of Guinea
and Red Sea. This investigation sheds light on the areas where
satellite AIS reception is congested by channel overload and
decoding procedures will hardly succeed in detecting packets.
Although no physical layer is taken into account in this result,
and therefore no advantages due to received power unbalance
of the packets for example is considered, the outcome of this
study is of interest when designing and operating a satellite
in order to identify regions where better (and more complex)
detection and decoding techniques are needed.
Moreover, two open questions arise from this investigation:
i) how sensible is the channel load variation due to change
in the receiver reception range, and ii) what is the best
receiver reception radius that maximises the AIS performance.
The answers to these two questions are the objective of the
reminder of this section. In order to address the first question,
we focus on the AIS channel load for a receiver with a
reception radius of 356 km which, as discussed earlier, covers
the cases of both AISat-1 and an airliner. The results are
presented in Fig. 6. In this case, the focus is on the North
America region. We can firstly note that maximum channel
load is now limited to less than 2 packets per slot, six times less
than in the reference LEO satellite configuration. Moreover,
only a limited number of spots close to the coast have a
relevant medium channel load and only in the north Mexican
Gulf and close to Seattle/Vancouver we exceed 1 packet per
slot. Interestingly, the drastic reduction in the reception radius
have a huge impact on the channel load. This reduced channel
load appears to be much more handleable from the system than
the one at the LEO satellite.
Fig. 6. Channel load G in packets per slot seen at an aircraft flying at 10
km altitude and assuming that it can receive packets from transmitters with at
least 0◦ elevation angle and equivalently this is the channel load for the DLR’s
satellite AISat-1 with the special antenna that reduces the receiver footprint.
As to the second question, we start by elaborating equation
(4). The total number n of ships falling within the reception
footprint can be derived once the average ship density per
square kilometer d is given. In particular, under the assumption
of a circular coverage area with radius r, the channel load can
be written as
vi
G = πr2 · d ·
ωi
fv (v)dv .
(6)
vi−1
i
In order to optimize the system performance, we focus on
the throughput, defined as the average number of packets
successfully retrieved per slot. Such a metric captures the
efficiency of a medium access protocol, and is a direct indicator
of how well a flying platform can exploit the available bandwidth. The slotted ALOHA (SA) model employed to describe
packets reception prompts the well-known characterization of
the throughput in terms G as S = Ge−G , so that the optimal
working point is achieved for unit average channel load2 .
Imposing this condition into (6), the optimum reception radius
follows:
1
ropt =
π·d·
i
ωi
vi
vi−1
.
(7)
fv (v)dv
The equation prompts how ropt depends in general, on the
specific receiver location and on the vessel speed distribution.
The average ship density can be related only with the receiver
coverage area or can also be more extended and can cover
wider earth surface areas. For example, we can identify an
oceanic area and we can perform the radius optimization for
that area. In our case we have identified the Atlantic Ocean
area delimited by latitudes in the range [15◦ N; 45◦ N] and
longitudes in the range [60◦ W; 15◦ W], where the optimum
radius is found to be ropt = 1252 km. In order to validate the
result of the optimization and to show how this optimization
2 We underline that the AIS standard does not foresee any feedback nor
retransmission. Therefore, the system modeled as SA is inherently stable.
k AIS messages per frame. Under the medium access model
we consider, the probability of detecting such a vessel within
one frame is given by 1 − (1 − e−G )k . On the other hand,
define as I(r) the number of frames during which the receiver
will illuminate the (ϕ, λ) location. Regarding transmission
patterns as independent across frames, the first pass detection
probability can then be expressed as
I(r)
1 − eGi (ϕ,λ,r)
Pdet (ϕ, λ, r) = 1 −
k
,
(8)
i=1
where Gi is the channel load seen at the receiver during the
i-th frame.
Fig. 7. SOTDMA throughput for a satellite with a reception range radius
of 1252 km. This is the radius which optimize the throughput in the north
Atlantic Ocean area.
will have an impact on the AIS system performance, we show
the throughput results for this reception radius.
In Fig. 7, the throughput results for a satellite with a
reception range of 1252 km in radius is shown. In order to
evaluate the throughput, we have computed the channel load
as per equation (4), for each Earth surface point. Recalling
that the maximum throughput of SA is 0.36 packets per slot,
reached for a channel load of 1 packet per slot, we can observe
that in the north Atlantic region the throughput is very close
to the maximum (above 0.3 packets per slot in the entire area),
validating the presented optimization procedure. On the other
hand, since the optimization is done exploiting the average
ship density in the north Atlantic Ocean the throughput cannot
match exactly the maximum in each point because variation in
the position dependant ship density with respect to the average
can be found. We can also observe that in some medium ship
populated regions as the west North America coast or the East
South America coast where the channel load in a conventional
LEO satellite would have been between 4 and 5 packets per
slot (Fig. 5), the throughput with the optimized reception range
is very close to the maximum, and therefore the channel load
can be deduced to be close to 1 packet per slot. This has
a direct advantage in the AIS system performance, because
higher throughput can be translated in the better ship tracking.
While throughput is apt to evaluate how efficiently resources are utilized, the primary goal of satellite AIS is to
offer worldwide tracking for vessels. Therefore, it is important
to also define a metric that captures the capability of detecting
ships for a receiver flying over a certain region. To this aim,
we introduce the first pass ship detection probability Pdet .
From this standpoint, it is relevant to observe that one flying
platform covers a point on the Earth surface for a certain
amount of time, determined by its orbit and speed. In terms of
vessel monitoring, then, it is paramount to identify the presence
of as many ships as possible within this interval, since the
subsequent passage over the same area may take place hours or
days after. Thus, we define as detection the event of decoding
at the receiver at least one packet sent by a vessel while
passing by its position. Let us consider a ship transmitting
As discussed, one of the main impairments for satellite
AIS is the high packet collision probability especially in
highly vessel populated regions. In this way, reducing the
reception radius of the satellite, with ad-hoc antenna design
as for the AISat-1 of DLR for example, could be of benefit.
On the other hand, the reduction of the reception radius of
the satellite diminishes the amount of time that the satellite
illuminates a certain point on Earth, reducing the packets
received (successfully or collided) belonging to a vessel in
that particular position. Furthermore, with smaller reception
radius, smaller portions of Earth surface are illuminated in each
pass and therefore less potential vessels can be tracked. There
is in fact, a tradeoff between the reduction of the reception
radius and the satellite AIS tracking performances that is well
captured by the Pdet metric. In particular the channel load G
will have a benefit from the footprint shrinking, but on the
other hand I(r) will be reduced compared to a common LEO
satellite.
In order to make a first analysis of this tradeoff, let us
consider a LEO satellite ground track as depicted in Fig. 8,
and three reception radiuses for the LEO satellite: i) r = 2500
km corresponding to a conventional satellite in Fig. 8(a); ii)
r = 1252 km corresponding to a satellite with the reception
radius optimized for oceanic regions in Fig. 8(b); iii) r = 712
km corresponding to DLR’s AISat-1 satellite Fig. 8(c). The
satellite ground track (red line in the figures) and the satellite
speed over ground is the same for all the cases. Furthermore,
we assume to capture the situation every minute, which corresponds to one AIS frame. The plots in Fig. 8 show the first
pass ship detection probability Pdet for the vessels with the
lowest packet transmission frequency, which are the vessels
sending one AIS packet every 180 s. Such a configuration
is of particular interest for two reasons. First, slow-moving
vessels are the ones less likely to be detected, so that Pdet for
them truly represents an indicator of how accurately a receiver
can depict the situation on the illuminated area. Secondly,
the transmission frequency of 1 message per 180 seconds
also characterizes VHF bands recently allocated to the socalled Long Range AIS (LRAIS). These additional channels
are foreseen explicitly for satellite reception, and vessels
are supposed to distribute AIS packets with the considered
frequency regardless of their speed. The reported study, then,
also offers hits on the impact of the reception radius on the
performance of LRAIS.
Moving back to the plots, in the case of conventional
satellite, the Earth surface area covered during one revolution
is the highest among all the three cases due to the biggest
(a) LEO satellite with a reception radius of 2500
km over its ground track. This represent the
reception radius of a standard commercial LEO
satellite.
(b) LEO satellite with a reception radius of 1252
km over its ground track. This reception radius
is the one which maximise the throughput for the
north Atlantic Ocean.
(c) LEO satellite with a reception radius of 712 km
over its ground track. This reception radius is the
one of the DLR’s AISat-1.
Fig. 8. First pass ship detection probability Pdet of ships with the lowest sending rate (1 packet sent every 180 s) for a LEO satellite with different reception
radiuses.
reception radius. On the other hand, we can observe that there
is a high portion of the ground track where the probability
of detection is very low. This is mainly concentrated in two
regions of the ground track, on the Atlantic Ocean, close to the
African and European coasts and in the Pacific Ocean close to
the Japanese and Australian coasts. This result suggests that
since these regions are densely populated with vessels, the
satellite footprint is too large to provide sufficient tracking
capabilities for this class of ships. In other words, the very
high channel load is driving the Pdet in this case. In the
second case in fact, when the reception radius is reduced to
half of the conventional satellite, the two regions of the ground
track where the Pdet is very low are much smaller, and the
vast majority of the ground track has a good or acceptable
vessel detection probability. It is also interesting to observe that
for some satellite positions close to the Arctic and Antarctic
regions, Pdet is exactly 0. This is due to the fact that no ships
are received in these regions and shall not be interpreted as a
negative result. In the last case, the satellite positions where the
Pdet is exactly 0 increases, due to the even smaller footprint.
On the other hand, in the Pacific Ocean region the first pass
vessel detection probability is less sensitive to the high density
and also in the Atlantic Ocean region, the locations where the
Pdet is reduced also w.r.t. the second case.
As a second example, we focus on a small portion of the
satellite ground track presented in Fig. 8, the region of the
Atlantic Ocean close to the African and European coasts. We
focus in this second case to the vessels with the highest packet
transmission frequency, which are the vessels sending one AIS
packet every 2 s. In Fig. 9 the results of the Pdet for this class
of vessels is shown for the three reception radius cases. What
we can observe is that while for a conventional LEO satellite
(red curve) also in the case of highest packet transmission
frequency, the Pdet drops to less than 0.2, for the optimized
reception radius it is higher than 0.95 while for the DLR’s
AISat-1 it does not fall below 1.
V.
C ONCLUSIONS
In this work we presented a general but simple and flexible
analytical framework for evaluating the channel load of the
SOTDMA protocol used in AIS. Exploiting realistic ship distribution over the sea surface and realistic ship speed distribution
we evaluated the channel load seen from different flying
Fig. 9. Ship detection probability Pdet of ships with the highest sending
rate (1 packet sent every 2 s) for a LEO satellite with three different reception
radiuses: 2500 km , 1252 km and 712 km. The satellite is passing close to
the African and European coasts on the Atlantic Ocean
objects. Common LEO satellites have the largest footprint but
suffer from very high channel loads, while aircraft as well
as the DLR’s AISat-1 due to the smaller footprint appears
to be more suitable for AIS data reception in heavily vessel
populated regions.
Exploiting the channel load model, an optimization of the
reception footprint is also presented in this work, in order to
show some possible use cases for the channel load model. The
optimized footprint is derived for an oceanic region (Atlantic
Ocean) and several results are shown in comparison with the
common LEO satellite footprint and DLR’s AISat-1 footprint.
The ship detection probability for the AIS packet class with
the lowest frequency of transmission, is shown to be very
high for both the optimized satellite and the DLR’s AISat1 assuming a common satellite ground track. On the other
hand, the former has the advantage to cover a larger area of
the Earth surface, increasing the number of vessels seen in
the first pass. Comparison with a common LEO satellite has
shown remarkable advantages of the two reduced footprints
especially in the densely vessel populated regions.
ACKNOWLEDGEMENTS
The research leading to these results has been carried out
under the framework of the project ”R&D for the maritime
safety and security and corresponding real time services”. The
project started in January 2013 and is led by the Program Coordination Defence and Security Research within the German
Aerospace Center (DLR).
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